StrainDB RAG

An RAG Model based on StrainsDB. The Database for the project was provided by Kenneth Reitz.

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Steps involved

Extracting and Validating the Data

• The SQLite DataBase consisted of 19 tables from which the strains_strain table was extracted.
  • Solution: extract data using SQLite module and create a Strain class (that inherits pydantic.BaseModel) for data validation
• Then the table's data cells consisted of lists in string format which needed to be converted back
  • Solution: eval("[1, 2, 3]") returns [1, 2, 3]
• The data needs to be saved.
  • Solution: dump data in JSON format.

Data Tokenization

• The Data was embedded into ChromaDB (persistent client) using the OpenAIEmbeddings function.
  • Solution: OpenAIEmbeddings function from the langchain_openai package.

Retrieval

• The data was retrieved from the ChromaDB using the Chroma class.